Apparatus and method for using model training and adaptation to detect furnace flooding or other conditions

ABSTRACT

A method includes obtaining data associated with operation of equipment in an industrial process and identifying training data and evaluation data in the obtained data. The method also includes, during each of multiple training periods, identifying one or more models and one or more first statistical values using at least some of the training data and determining a threshold value using the one or more first statistical values. The one or more models represent the operation of the equipment. The method further includes, during each of multiple evaluation periods, determining one or more second statistical values using at least some of the evaluation data and the one or more models, comparing the one or more second statistical values to the threshold value determined in a preceding one of the training periods, and determining whether the equipment is suffering from at least one specified condition based on the comparison. In addition, the method includes, in response to determining that the equipment is suffering from the at least one specified condition, generating an alert identifying the at least one specified condition.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY CLAIM

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/489,006 and U.S. ProvisionalPatent Application No. 62/489,028 filed on Apr. 24, 2017. Both of theseprovisional applications are hereby incorporated by reference in theirentirety.

TECHNICAL FIELD

This disclosure generally relates to the monitoring of furnaces or otherindustrial process equipment. More specifically, this disclosure relatesto an apparatus and method for using model training and adaptation todetect furnace flooding or other conditions.

BACKGROUND

Furnaces are used in a variety of industries and in a variety of ways toprovide heating. For example, industrial processes in oil and gasrefineries, chemical plants, or other industrial facilities often usefurnaces to heat materials in order to facilitate desired chemicalreactions. A furnace typically operates by receiving flows of fuel (suchas fuel gas, fuel oil, coal, or wood chips) and inlet air, and the fuelis combusted in the presence of the inlet air to produce heat that istransferred to a process material. Ideally, the combustion of the fueland the heating of the process material remain stable, and all orsubstantially all of the fuel entering the furnace is combusted.

Furnace flooding refers to a condition that can occur when thecombustion of fuel gas in a furnace becomes unstable, such as when aratio of the inlet air flow to the fuel gas flow moves outside of thefurnace's operating envelope. When this occurs, the combustion processcan become unstable or even stop, resulting in a loss of flame withinthe furnace. The loss of flame means that no fuel gas is being burnedwithin the furnace. However, fuel gas may continue to be provided intothe furnace, resulting in a build-up of uncombusted fuel gas in thefurnace. In some circumstances, this could lead to an explosion of thefurnace.

SUMMARY

This disclosure provides an apparatus and method for using modeltraining and adaptation to detect furnace flooding or other conditions.

In a first embodiment, a method includes obtaining data associated withoperation of equipment in an industrial process and identifying trainingdata and evaluation data in the obtained data. The method also includes,during each of multiple training periods, identifying one or more modelsand one or more first statistical values using at least some of thetraining data and determining a threshold value using the one or morefirst statistical values. The one or more models represent the operationof the equipment. The method further includes, during each of multipleevaluation periods, determining one or more second statistical valuesusing at least some of the evaluation data and the one or more models,comparing the one or more second statistical values to the thresholdvalue determined in a preceding one of the training periods, anddetermining whether the equipment is suffering from at least onespecified condition based on the comparison. In addition, the methodincludes, in response to determining that the equipment is sufferingfrom the at least one specified condition, generating an alertidentifying the at least one specified condition.

In a second embodiment, an apparatus includes at least one processingdevice configured to obtain data associated with operation of equipmentin an industrial process and identify training data and evaluation datain the obtained data. The at least one processing device is alsoconfigured to, during each of multiple training periods, identify one ormore models and one or more first statistical values using at least someof the training data and determine a threshold value using the one ormore first statistical values. The one or more models represent theoperation of the equipment. The at least one processing device isfurther configured to, during each of multiple evaluation periods,determine one or more second statistical values using at least some ofthe evaluation data and the one or more models, compare the one or moresecond statistical values to the threshold value determined in apreceding one of the training periods, and determine whether theequipment is suffering from at least one specified condition based onthe comparison. In addition, the at least one processing device isconfigured to, in response to determining that the equipment issuffering from the at least one specified condition, generate an alertidentifying the at least one specified condition.

In a third embodiment, a non-transitory computer readable mediumcontains instructions that when executed cause at least one processingdevice to obtain data associated with operation of equipment in anindustrial process and identify training data and evaluation data in theobtained data. The medium also contains instructions that when executedcause the at least one processing device to, during each of multipletraining periods, identify one or more models and one or more firststatistical values using at least some of the training data anddetermine a threshold value using the one or more first statisticalvalues. The one or more models represent the operation of the equipment.The medium further contains instructions that when executed cause the atleast one processing device to, during each of multiple evaluationperiods, determine one or more second statistical values using at leastsome of the evaluation data and the one or more models, compare the oneor more second statistical values to the threshold value determined in apreceding one of the training periods, and determine whether theequipment is suffering from at least one specified condition based onthe comparison. In addition, the medium contains instructions that whenexecuted cause the at least one processing device to, in response todetermining that the equipment is suffering from the at least onespecified condition, generate an alert identifying the at least onespecified condition.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example system for detection of furnace floodingor other conditions according to this disclosure;

FIG. 2 illustrates an example data flow supporting model training andadaptation to detect furnace flooding or other conditions according tothis disclosure;

FIG. 3 illustrates an example device for detection of furnace floodingor other conditions according to this disclosure;

FIG. 4 illustrates an example method for detection of furnace floodingor other conditions according to this disclosure;

FIG. 5 illustrates an example technique for retraining a model used todetect furnace flooding or other conditions according to thisdisclosure; and

FIG. 6 illustrates an example of second stage processing used to detectfurnace flooding or other conditions according to this disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 6, discussed below, and the various embodiments used todescribe the principles of the present invention in this patent documentare by way of illustration only and should not be construed in any wayto limit the scope of the invention. Those skilled in the art willunderstand that the principles of the invention may be implemented inany type of suitably arranged device or system.

FIG. 1 illustrates an example system 100 for detection of furnaceflooding or other conditions according to this disclosure. As shown inFIG. 1, the system 100 includes or operates in conjunction with afurnace 102. The furnace 102 generally operates by receiving at leastone fuel gas flow and at least one inlet air flow. The fuel gas isignited within the furnace 102 and burns in the presence of oxygencontained in the inlet air, thereby producing heat. The generated heatcan be used to heat one or more materials, such as one or more flows offluid (like one or more gases or liquids) in a process flow.

In the example shown in FIG. 1, the furnace 102 includes a radiantsection 104, a convection section 106, a shield section 108, a breech110, and a stack 112. The radiant section 104 is generally configured totransfer radiant heat into one or more materials being heated, while theconvection section 106 is generally configured to pre-heat the one ormore materials before the materials enter the radiant section 104. Theshield section 108 generally separates the radiant section 104 from theconvection section 106 and helps to protect the convection section 106from direct radiant heating. The breech 110 generally denotes thetransition from the convection section 106 to the stack 112, and thestack 112 generally allows exhaust to exit the furnace 102.

The radiant section 104 of the furnace 102 in FIG. 1 includes one ormore burners 114, which are configured to ignite fuel gas entering thefurnace 102. The heat created when the fuel gas burns radiates into oneor more radiant tubes 116, which contain the one or more materials beingheated. A bridgewall 118 divides the lower portion of the radiantsection 104 into different spaces to facilitate more effective heatingby the burners 114. Each burner 114 includes any suitable structure forigniting and burning fuel gas. Each radiant tube 116 includes anysuitable structure for transporting material that is being heated. Thebridgewall 118 includes any suitable structure for dividing a space.

The convection section 106 and the shield section 108 of the furnace 102in FIG. 1 include one or more coils 120, which are connected to the oneor more radiant tubes 116 via one or more crossovers 122. The coils 120receive the one or more materials to be heated through one or moreinlets 124, and the materials travel through the coil(s) 120 to theradiant tube(s) 116 before exiting through one or more outlets 126. Thecoils 120 can travel back and forth in the space between the radiantsection 104 and the stack 112. By passing the one or more materialsthrough the coils 120, the materials can be pre-heated in the convectionsection 106 before the materials are heated in the radiant section 104.Each coil 120 includes any suitable structure for transporting materialbeing heated. Each crossover 122 includes any suitable structure forlinking a coil and a radiant tube.

A stack damper 128 is located at or near the top of the furnace 102 andis used to control the flow of exhaust out of the furnace 102 throughthe stack 112. For example, the stack damper 128 could denote a flatcircular, square, or other structure that can be rotated to change thesize of a passageway through the stack 112. Similarly, a plenum damper130 is located at or near the bottom of the furnace 102, such as withina plenum chamber 132. The plenum damper 130 is used to control the flowof inlet air into the furnace 102. The plenum damper 130 could denote aflat circular, square, or other structure that can be rotated to changethe size of a passageway through the plenum chamber 132. The plenumchamber 132 denotes an area where fuel gas and inlet air are receivedand mixed before entering the furnace 102. A valve 134 or otherstructure could be used to control the flow of fuel gas into the furnace102 at or near the bottom of the furnace 102, such as into the plenumchamber 132. Each damper 128 and 130 includes any suitable structure forcontrolling fluid flow. The plenum chamber 132 includes any suitablestructure for receiving and providing fluid. The valve 134 includes anysuitable structure for controlling a fuel gas flow.

Various sensors can be positioned within or otherwise used inconjunction with the furnace 102. For example, one or more draft gauges136 could be used to measure airflow through one or more portions of thefurnace 102. One or more oxygen sensors 138 could be used to measure theoxygen level at one or more locations within the furnace 102. One ormore pressure sensors 140 could be used to measure the pressure level atone or more locations within the furnace 102. One or more temperaturesensors 142 could be used to measure the temperature at one or morelocations within the furnace 102 or to measure the temperature of aprocess fluid (the material being heated by the furnace 102). One ormore sensors 144 could be used to measure an amount of combustiblematerial at one or more locations within of the furnace 102. One or moresensors 146 could be used to measure flows of material (such as fuel orair) into the furnace 102.

Each of the sensors 136-146 includes any suitable structure formeasuring one or more characteristics in or associated with a furnace.As particular examples, the sensors could include THERMOX combustionanalyzers or combustion analyzers using tunable diode lasers. Note thatthe numbers and positions of the various types of sensors in FIG. 1 arefor illustration only. Any number of each type of sensor and anysuitable arrangement of those sensors could be used in the furnace 102.Also note that any other or additional types of sensors could be used inthe furnace 102.

This represents a brief description of one type of furnace 102 that maybe used to produce heat. Additional details regarding this type offurnace 102 are well-known in the art and are not needed for anunderstanding of this disclosure. Note that the general structure of thefurnace 102 shown in FIG. 1 is for illustration only. Furnaces can comein a wide variety of designs and configurations, and the example of thefurnace 102 shown in FIG. 1 is for illustration only. Other types offurnaces could be used here, such as furnaces that operate using otherfuels like fuel oil, coal, or wood chips.

The system 100 may also include at least one controller 148 that is usedto control various aspects of the furnace's operation. For example,industrial equipment is typically associated with controlled,manipulated, and disturbance variables. A controlled variable generallydenotes a variable whose value can be measured or inferred and that iscontrolled to be at or near a desired setpoint or within a desiredrange. A manipulated variable generally denotes a variable that can bealtered in order to adjust one or more controlled variables. Adisturbance variable generally denotes a variable whose value can beconsidered but not controlled when determining how to adjust one or moremanipulated variables to achieve desired changes to one or morecontrolled variables.

The controller 148 receives measurements from the various sensors136-146, as well as setpoints for various controlled variables. Thecontroller 148 uses this information to generate control signals foradjusting the operation of the furnace 102. For example, the controller148 could receive pressure measurements and a pressure setpoint and,based on differences between the two, generate a control signal to varythe position or opening of the stack damper 128. The controller 148could also receive oxygen level measurements and an oxygen levelsetpoint and, based on differences between the two, generate a controlsignal to vary the position or opening of the plenum damper 130. Thecontroller 148 could further receive temperature measurements and atemperature setpoint and, based on differences between the two, generatea control signal to vary the amount of fuel gas entering the furnace102, such as by adjusting the valve 134 that controls the fuel gas flow.Other or additional operations could also occur using the controller 148to control other or additional aspects of the furnace 102.

The controller 148 includes any suitable structure for controlling oneor more characteristics associated with a furnace or other industrialequipment. The controller 148 could, for example, represent aproportional-integral-derivative controller or a multivariablecontroller, such as a controller implementing model predictive controlor other advanced predictive control. As a particular example, thecontroller 148 could represent a computing device running a real-timeoperating system, a WINDOWS operating system, or other operating system.

Note that while one controller 148 is shown here, other numbers ofcontrollers could also be used. For example, separate controllers couldbe used to control separate aspects of the furnace 102, such as whendifferent controllers are used in different single-input, single-output(SISO) control loops or other control loops. Also note that multiplecontrollers could be arranged hierarchically, such as when one or morelower-level controllers execute control logic for controlling theequipment and one or more higher-level controllers execute planning,optimization, or other functions. Further note that the use of acontroller is not specifically required here. As long as there is sometype of microprocessor, edge device, data acquisition system, or otherdevice/system that can collect data associated with operation of thefurnace 102 or other equipment for use in analyzing the equipment, itmay be immaterial how many controllers are used in the system 100.

Operator access to and interaction with the controller 148 and othercomponents of the system 100 can occur via one or more operator consoles150. Each operator console 150 could be used to provide information toan operator and receive information from an operator. For example, eachoperator console 150 could provide information identifying a currentstate of industrial equipment to the operator, such as values of variousprocess variables and warnings, alarms, or other states associated withthe furnace 102 or other equipment. Each operator console 150 could alsoreceive information affecting how the furnace 102 or other equipment iscontrolled, such as by receiving setpoints for process variablescontrolled by the controller 148 or other information that alters oraffects how the controller 148 control the furnace 102 or otherequipment. Each operator console 150 includes any suitable structure fordisplaying information to and interacting with an operator. Eachoperator console 150 could, for instance, represent a computing devicerunning a WINDOWS operating system or other operating system.

At least one network 152 couples the controller 148, operator console150, sensors, actuators, and other components in the system 100. Eachnetwork 152 facilitates the transport of information between differentcomponents. For example, the network 152 could transport measurementdata from the sensors and provide control signals to the actuators ofthe furnace 102. The network 152 could represent any suitable network orcombination of networks. As particular examples, the network 152 couldrepresent at least one Ethernet network, electrical signal network (suchas a HART network), pneumatic control signal network, or any other oradditional type(s) of network(s).

As described above, furnace flooding can occur when the combustion offuel gas in the furnace 102 becomes unstable, such as when a ratio ofthe inlet air flow to the fuel gas flow moves outside of the furnace'soperating envelope. Various causes may exist for furnace flooding. Forexample, if an oxygen sensor 138 in the furnace 102 clogs or otherwisefails to operate correctly, the oxygen sensor 138 could generate oxygenlevel measurements that are higher than the actual oxygen level. Thismay cause the controller 148 to close the plenum damper 130 more thanneeded, which reduces the amount of inlet air (and therefore oxygen) inthe furnace 102 and can cause the combustion to become unstable. Whenthis occurs, a loss of flame within the furnace 102 can occur.

Furnace flooding can lead to a dangerous explosive event, and operatorsoften need to react quickly to any indicator of potential furnaceflooding. In one conventional approach, a reduction in excess oxygengas, an elevated level of unburned fuel (called combustibles), or acombination of these are used as a leading indicator of a floodingevent. An operator may react to either condition by reducing fuel flowand increasing air flow within the furnace 102. This usually mitigatesthe conditions that could lead to a furnace explosion, but these leadingindicators often come late in the process and sometimes do not leaveoperators sufficient time to react. Moreover, the operators' efforts toresolve these conditions sometimes require or result in an abruptshutdown of the furnace 102, causing production disruption in otherparts of an industrial facility (such as a refinery) and wasted product.Furnace flooding could occur more frequently during a furnace startupafter a shutdown since the startup is often a highly manual process.

In another conventional approach, anomaly detection models based onPrincipal Components Analysis (PCA) are used to detect potential furnaceflooding. However, these models are often specific to one particularfurnace, meaning there is no ability to transfer a model from onefurnace to another. Moreover, these models are only mathematicalestimations of the behavior of a furnace or other industrial equipment.Changes in the industrial equipment over time (which may be common inmany industries) can render a model unsuitable for use even with thefurnace for which that model was specifically designed.

As described in more detail below, this disclosure provides techniquesfor reliable on-line prediction of furnace flooding or other equipmentconditions. Among other things, these techniques can be used to generatetimely alerts in response to potential flooding conditions or otherequipment conditions. The alerts could typically be generated withenough lead time for operators to make necessary adjustments, such as atleast ten to twenty minutes before actual flooding. This reduces thenumber of potentially hazardous occurrences and allows a safe controlledresponse to move a furnace or other equipment to a safe operatingcondition. In many cases, this can be accomplished without taking theequipment down entirely, meaning no shutdown and therefore nosignificant process interruptions occur.

Also, this approach supports the retraining of one or more models usedfor the detection of furnace flooding or other conditions. As a result,this approach is transferable among multiple furnaces or other equipmentand adjusts to changing conditions within the same equipment.

Further, the techniques described below can support multiple-stagedeterminations of whether flooding or other conditions are or might beoccurring. In an unbalanced data situation (such as when only a fewfurnace floods occur a year and non-flood operational conditions existfor the remainder of time), one-stage solutions could potentiallyproduce a high number of false positives, meaning they indicate thatflooding is occurring when it is actually not. This is often anannoyance for operators, who end up ignoring these alarms. By separatingvarious tasks into separate stages (such as the identification ofanomalous conditions in one stage and the separation of flood-leadingand non-flood-leading events in another stage), the techniques in thisdocument provide operators with fewer false positive alerts. This allowsthe operators to follow through on the actual alerts that are generated.

In addition, because furnaces can be used in facilities like oil and gasrefineries, any shutdown of a furnace can have significant economicimpacts on the owners or operators of the facilities. By reducing thenumber of furnace shutdowns, the techniques described in this documentcan provide significant economic benefits for the owners or operators.

Note that while often described as being used to detect furnaceflooding, the same techniques provided in this document can be used withother industrial equipment. For example, these techniques could be usedwith industrial equipment that typically achieves an equilibrium duringnormal operation. The techniques described below can then operate todetect when there is an unexpected deviation from this equilibrium. Thisincludes furnaces that operate using other fuels like fuel oil, coal, orwood chips. While furnace flooding may not be an issue in these types offurnaces, the techniques described below could still be used to identifyproblems with those furnaces.

Additional details regarding this furnace flooding or other conditiondetection functionality are provided below. Note that this conditiondetection functionality could be implemented in any suitable manner. Forexample, the condition detection functionality could be implementedusing software or firmware instructions that are executed by one or moreprocessors of a computing device, such as a desktop, laptop, server, ortablet computer. As a particular example, the condition detectionfunctionality could be implemented by the controller 148, the operatorstation 150, or another device within the system 100 (such as anotherdevice coupled to the network 152 or to another network). The conditiondetection functionality could also be implemented outside of the system100, such as in a remote server, a cloud-based environment, or any othercomputing system or environment communicatively coupled to the system100.

Although FIG. 1 illustrates one example of a system 100 for detection offurnace flooding or other conditions, various changes may be made toFIG. 1. For example, the system 100 could include any number offurnaces, sensors, actuators, controllers, operator stations, networks,and other components. Also, the makeup and arrangement of the system 100in FIG. 1 is for illustration only. Components could be added, omitted,combined, further subdivided, or placed in any other suitableconfiguration according to particular needs. Further, particularfunctions have been described as being performed by particularcomponents of the system 100. This is for illustration only. In general,control or automation systems are highly configurable and can beconfigured in any suitable manner according to particular needs. Inaddition, FIG. 1 illustrates one example operational environment wheredetection of a problem with equipment can be used. This functionalitycan be used in any other suitable system, and the system need notinclude a furnace.

FIG. 2 illustrates an example data flow 200 supporting model trainingand adaptation to detect furnace flooding or other conditions accordingto this disclosure. The data flow 200 could, for example, be supportedin the system 100 to detect furnace flooding with the furnace 102.However, the data flow 200 could be used with any other suitable systemand with any other suitable process equipment.

As shown in FIG. 2, the data flow 200 includes the use of varioussensors 202, which provide sensor measurements or other data to aprocess data acquisition system 204. The sensors 202 could denote thevarious sensors 136-146 used in or with the furnace 102 of FIG. 1 orother sensors used with process equipment being monitored. The processdata acquisition system 204 collects the data from the sensors 202, suchas via wired or wireless connections. The process data acquisitionsystem 204 can also provide at least some of the data to a process datahistorian 206 for longer-term storage. The process data acquisitionsystem 204 includes any suitable structure configured to collect datafrom multiple sensors. The process data historian 206 includes anysuitable structure configured to store process data, including at leastsome of the data from the sensors 202.

A process monitoring and runtime analytics system 208 processes the datafrom the sensors 202 to determine whether the process equipment beingmonitored is suffering or potentially suffering from one or moreconditions, such as furnace flooding. As shown in FIG. 2, the system 208supports or executes a data cleansing function 210, a datacontextualization function 212, and a detection algorithm 214. Thecleansing function 210 generally operates to receive data from thesensors 202 via the process data acquisition system 204 and/or theprocess data historian 206. The cleansing function 210 filters orotherwise processes the data from the sensors 202 to identify data thatis suitable for use in training the detection algorithm 214 or beingevaluated by the detection algorithm 214. Any suitable filtering can besupported by the cleansing function 210.

The data contextualization function 212 processes the data to identifydata that can be used for training the detection algorithm 214 (duringtraining periods) and data that can be used to evaluate whether processequipment is suffering from a specified condition (during evaluationperiods). The data contextualization function 212 can use any suitablecriteria to select data for training or evaluation. Example criteria areprovided below, although other criteria could also be used.

The detection algorithm 214 processes the data provided via the datacontextualization function 212. The detection algorithm 214 uses thedata to determine whether a furnace or other equipment is suffering fromflooding or other issues. As described in more detail below, thedetection algorithm 214 generates at least one model of the equipmentbeing monitored using training data and detects problems with theequipment using the model(s) and evaluation data. The detectionalgorithm 214 also retrains and adapts its model(s) to account forchanging conditions with the equipment. This also allows the detectionalgorithm 214 to be used across multiple furnaces or other equipment.

The detection algorithm 214 can output predictions identifying furnaceflooding or other conditions. The predictions could be used in anysuitable manner. In this example, the predictions are provided to anevent detection function 216, which determines when specified events(such as positive predictions identifying actual or potential floodingor other conditions) are output by the detection algorithm 214. Theevent detection function 216 can then provide an identity of the eventsto an event notification function 218 and/or an event visualizationfunction 220. The event notification function 218 generates eventnotification messages that are transmitted to one or more mobile devicesor other devices associated with one or more users 222. The eventvisualization function 220 generates event notification messages thatare transmitted to one or more display devices 224, which could beassociated with one or more operator consoles 150. The eventnotification messages can identify warnings, alarms, or othernotifications associated with detected flooding events or other events.

Each function 210-220 of the process monitoring and runtime analyticssystem 208 could be implemented in any suitable manner. For example, theprocess monitoring and runtime analytics system 208 could be implementedusing one or more computing devices, and the functions 210-220 could beimplemented using software instructions executed by the computingdevices. Note that any number of computing devices could be used, suchas when different computing devices perform different functions orsubsets of functions. Also note that the computing devices need not formpart of the system in which the process equipment that is beingmonitored is located. For instance, the process monitoring and runtimeanalytics system 208 could be implemented using a remote server, acloud-based environment, or other computing system or environmentconfigured to receive the data from the sensors 202 and initiatenotifications.

Although FIG. 2 illustrates one example of a data flow 200 supportingmodel training and adaptation to detect furnace flooding or otherconditions, various changes may be made to FIG. 2. For example, thefunctionality of the process monitoring and runtime analytics system 208could be implemented in any other suitable manner.

FIG. 3 illustrates an example device 300 for detection of furnaceflooding or other conditions according to this disclosure. The device300 could, for example, denote the controller 148, operator station 150,or other device within or used in conjunction with the system 100 inFIG. 1. The device 300 could also implement the process monitoring andruntime analytics system 208 of FIG. 2. However, the device 300 could beused in any other suitable system, and the process monitoring andruntime analytics system 208 could be implemented in any other suitablemanner.

As shown in FIG. 3, the device 300 includes at least one processor 302,at least one storage device 304, at least one communications unit 306,and at least one input/output (I/O) unit 308. Each processor 302 canexecute instructions, such as those that may be loaded into a memory310. The instructions could implement the furnace flooding or othercondition detection functionality described in this patent document.Each processor 302 denotes any suitable processing device, such as oneor more microprocessors, microcontrollers, digital signal processors,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), or discrete circuitry.

The memory 310 and a persistent storage 312 are examples of storagedevices 304, which represent any structure(s) capable of storing andfacilitating retrieval of information (such as data, program code,and/or other suitable information on a temporary or permanent basis).The memory 310 may represent a random access memory or any othersuitable volatile or non-volatile storage device(s). The persistentstorage 312 may contain one or more components or devices supportinglonger-term storage of data, such as a read only memory, hard drive,Flash memory, or optical disc.

The communications unit 306 supports communications with other systemsor devices. For example, the communications unit 306 could include anetwork interface card or a wireless transceiver facilitatingcommunications over a wired or wireless network. The communications unit306 may support communications through any suitable physical or wirelesscommunication link(s).

The I/O unit 308 allows for input and output of data. For example, theI/O unit 308 may provide a connection for user input through a keyboard,mouse, keypad, touchscreen, or other suitable input device. The I/O unit308 may also send output to a display, printer, or other suitable outputdevice.

Although FIG. 3 illustrates one example of a device 300 for detection offurnace flooding or other conditions, various changes may be made toFIG. 3. For example, components could be added, omitted, combined,further subdivided, or placed in any other suitable configurationaccording to particular needs. Also, computing devices can come in awide variety of configurations, and FIG. 3 does not limit thisdisclosure to any particular configuration of computing device.

FIG. 4 illustrates an example method 400 for detection of furnaceflooding or other conditions according to this disclosure. For ease ofexplanation, the method 400 may be described as being performed by thedevice 300 of FIG. 3 supporting the process monitoring and runtimeanalytics system 208 of FIG. 2 in the system 100 of FIG. 1. However, themethod 400 could be used with any other suitable device and in any othersuitable system (even those without furnaces).

As shown in FIG. 4, the method 400 includes collecting data related to afurnace or other industrial equipment from one or more client devices402. This could include, for example, the processor 302 receiving thedata from the sensors 202 via the process data acquisition system 204and/or the process data historian 206. The collected data can includespecific information related to operation of a furnace or otherindustrial equipment, such as data related to certain process variables.The actual process variable data that is collected in this step could beselected based on the domain expertise of personnel familiar with thespecific industrial equipment to be monitored.

In the particular example shown in FIG. 4, the client devices 402 couldinclude at least one PLANT INFORMATION (PI) server from HONEYWELLINTERNATIONAL INC. or other data collection device(s). The PI servercould denote a process historian or other device that collectsinformation associated with an industrial process over time, such assensor measurements, actuator control signals, or other data. The clientdevices 402 could also include at least one monitoring application thatcollects the data from one or more controllers 148 or other devices. Anexample monitoring application is the UNIFORMANCE ASSET SENTINELsoftware from HONEYWELL INTERNATIONAL INC.

The method 400 generally includes a “stage one” training process 404, a“stage two” training process 406, and an evaluation process 408. Thestage one training process 404 generally collects historical data 410associated with operation of industrial equipment being monitored. Thehistorical data 410 includes data from the client devices 402. Thehistorical data 410 could be pre-processed in any suitable manner toprepare the data for use in the stage one training process 404. Forexample, the data could be synchronized, such as by synchronizing thetime stamps of the data to organize the data over time. This may benecessary, for instance, when different sensors collect data atdifferent frequencies, and the data could be mapped to a common timescale (such as one-minute intervals).

The stage one training process 404 generally denotes the operations usedto train one or more models for detecting abnormal conditions with theequipment being monitored. One or more rules 412 can be used to identifywhen training or evaluation is to occur. The rules 412 could, forexample, specify operational rules or ranges for a subset of theselected process variables. The ranges can be used as guidelines forselecting training data, which is used in the stage one training process404 to generate the one or more models.

The rules 412 are used to select training data during operation 414, andthe resulting training data is stored in one or more data tables orother files 416. Statistics for the training data are estimated duringoperation 418, and the statistics are stored as signal parameters 420.The training data is also normalized based on the signal parametersduring operation 422, and the resulting data is used to generate one ormore stage one models 424 and at least one threshold 426 (which are bothused in the evaluation process 408).

In some embodiments, the stage one training process 404 could beimplemented as follows. Normal distribution parameters for each processvariable signal are estimated and stored as the signal parameters.Normalization of each process variable signal is performed, such asbased on the following.

$\begin{matrix}{X_{t}^{i} = \frac{Z_{t}^{i} - \mu_{t}^{Z^{i}}}{\sigma^{Z^{i}}}} & (1)\end{matrix}$Here, i denotes the i^(th) signal being observed, and t denotes an indexsignifying a single arbitrary time instant. Also, Z denotes a rawsignal, X denotes a normalized signal, μ denotes an expected value oraverage value, and a denotes a standard deviation across the trainingdata (where these values are signal-specific as indicated by the isuperscript). A multivariate regression is formulated for each processvariable signal, where a target process variable signal is a dependentvariable and the remaining process variable signals are the independentvariables. An example of this can be expressed as:

$\begin{matrix}{X_{t}^{i} = {{{\hat{X}}_{t}^{i} + ɛ_{t}^{i}} = {\beta_{0} + {\sum\limits_{j!=i}{\beta_{j}X_{t}^{j}}} + ɛ_{t}^{i}}}} & (2)\end{matrix}$Here, the set of β values denotes the coefficients of the models, and εdenotes the residual errors (residual process) for each time instant foreach signal. This set of regression equations describes the stage onemodels being estimated in step 424. The regression equations generatedby the multivariate regression define an equilibrium state of theequipment being monitored. The coefficients for the set of multivariateregressions are estimated and stored. The standard deviation of theresulting residuals are obtained and stored for each equation, and thecorresponding residuals are rescaled. This could be expressed as:

$\begin{matrix}{\epsilon_{t}^{i} = \frac{ɛ_{t}^{i}}{\sigma^{ɛ^{i}}}} & (3)\end{matrix}$Here, ϵ denotes the rescaled residual errors, and σ denotes the standarddeviation for the specific residual process. For every time instant inthe training data, the sum of the squared (scaled) residuals iscalculated. This sum, for each time instant, is referred to as theQ-statistic and could be expressed as follows.

$\begin{matrix}{Q_{t} = {\sum\limits_{i = 1}^{n}\left( \epsilon_{t}^{i} \right)^{2}}} & (4)\end{matrix}$Here, Q_(t) denotes the value of the Q-statistic at time t. The normaldistribution parameters, mean, and standard deviation of the Q-statisticacross the training data are determined. Using these parameters and achosen alpha-quantile (ρ-value), a statistical threshold for theQ-statistic can be computed. The statistical threshold for theQ-statistic could be expressed as:Q=μ ^(Q) −F _(Φ) ⁻¹(ρ)σ^(Q)  (5)Here, Q denotes the threshold value, μ^(Q) denotes the expected oraverage value, and σ^(Q) denotes the standard deviation of theQ-statistic across the training data. Also, F_(Φ) ⁻¹(ρ) denotes theinverse of the cumulative distribution function (otherwise known as thequantile function), and it returns a quantile value for a givenprobability ρ. The Φ subscript indicates that this represents thequantile function of a standard normal distribution. The threshold valueQ is used in the evaluation process 408.

The results of the stage one training process 404 can include a set ofequations that model the equilibrium of the equipment being monitored.Each equation can describe the variability of one process variablesignal, which is estimated by the behaviors of the other processvariable signals. The equations are estimated (trained) under “normal”operation as described above. The results of the stage one trainingprocess 404 also include the Q-statistic threshold, which defines avalue above which there may be instability in the equipment beingmonitored.

The first part of the evaluation process 408 uses “live” evaluation datathat is stored in one or more data tables or other files 436. Theevaluation data could, for example, denote real-time data related tooperation of the equipment being monitored. In particular embodiments,the evaluation data could denote data received in JavaScript ObjectNotation (JSON) payloads. The evaluation data is evaluated during astage one evaluation operation 438 in order to generate statisticalvalues 440 for the evaluation data. The statistical values 440 arecompared to the threshold value 426 to determine whether stage onealerts 428 should be generated indicative of furnace flooding or otherproblem.

In some embodiments, this portion of the evaluation process 408 could beimplemented as follows. Incoming process variable signals are normalizedusing the signal parameters 420 that were estimated during the stage onetraining process 404. This could be expressed as follows.

$\begin{matrix}{X_{t}^{i} = \frac{Z_{t}^{i} - \mu_{t}^{Z^{i}}}{\sigma^{Z^{i}}}} & (6)\end{matrix}$This represents the same type of calculation described above. Aprediction for each process variable signal is obtained and is based onthe estimated regression coefficients. The prediction could be expressedas:

$\begin{matrix}{{\hat{X}}_{t}^{i} = {\beta_{0} + {\sum\limits_{j!=i}{\beta_{j}X_{t}^{j}}}}} & (7)\end{matrix}$Here, {circumflex over (X)} denotes a predicted value of the i^(th)signal at time t based on the other signals and the previously-estimatedβ coefficients. Errors are calculated based on the instantaneouspredictions and the actual normalized signals, and the errors arerescaled based on the residual standard deviations estimated during thestage one training process 404. This could include obtaining a residualmatrix by subtracting the predictions from the actual signals andrescaling the residuals. The residual matrix could be determined asfollows:

$\begin{matrix}{{X_{t}^{i} - {\hat{X}}_{t}^{i}} = \begin{bmatrix}ɛ_{t}^{I} \\\vdots \\ɛ_{t}^{n}\end{bmatrix}} & (8)\end{matrix}$Here, the matrix on the right denotes the previously-mentioned errorsfor each signal in vector form. The residuals could be rescaled asfollows:

$\begin{matrix}{\epsilon_{t}^{i} = \frac{ɛ_{t}^{i}}{\sigma^{ɛ^{i}}}} & (9)\end{matrix}$An instantaneous Q-statistic can be calculated as follows.

$\begin{matrix}{Q_{t} = {\sum\limits_{i = 1}^{n}\left( \epsilon_{t}^{i} \right)^{2}}} & (10)\end{matrix}$The instantaneous Q-statistic is compared to the threshold Q. If thethreshold is exceeded, this indicates a departure from “normal”operation, and a stage one alert 428 can be triggered.

Note that the method 400 could end here without the stage two trainingprocess 406 or the remaining portion of the evaluation process 408. Inthis case, any stage one alerts could be used to generate a warning,alarm, or other output to one or more human operators. However, asdescribed above, false positives can be a problem in unbalanced datasituations. False positives are undesirable because they can cause humanoperators to ignore false (incorrect) positives and possibly miss true(correct) positives. To help combat this, the stage two training process406 and the remaining portion of the evaluation process 408 can beperformed to classify the stage one alerts into true positive events andfalse positive events.

The stage two training process 406 here generates one or more additionalmodels used by the evaluation process 408. During the stage two trainingprocess 406, second stage features are selected during operation 430,and second stage data associated with the selected features is stored inone or more data tables or other files 432. The selection of the secondstage features combines data from the designated process variablesignals to be monitored with the estimated statistical threshold and thecalculated Q-statistic values for each time instant. From this augmenteddata, all observations within the stage one data segments that do notresult in a flooding event are assigned to a first class. Of the datasegments that do result in flooding events, observations prior to theflooding events are assigned to a second class. In some embodiments,only stage one alerts 428 that last for at least a specified period oftime (such as five minutes) are used for stage two training.

At least one second stage model 434 is generated using the non-discardeddata. In some embodiments, this could involve estimating and fitting asupport vector machine (SVM) classifier model onto the stage one alerts.A support vector machine generally operates to find a hyperplane thatseparates two classes of objects. In this case, the idea of thehyperplane is to separate true positive alarms and false positive alarmswhen the Q-statistic values are signaling a lack of normality. Becauseit is not always possible to find a hyperplane in a space defined by thedimensions of a problem, the data can be mapped to a higher-dimensionalfeature space. A hyperplane is then selected that maximizes the distancebetween that hyperplane and the furthest point of each separated class.In some embodiments, this can be accomplished by the use of a kernelfunction, which enables distance calculations in the high-dimensionalfeature space without ever computing the coordinates of the data in thatspace (this is known as the “kernel trick”). The kernel function chosenfor this classifier could be a radial basis function. A formalrepresentation of this approach could be expressed as:

$\begin{matrix}{{\min\limits_{\alpha}{\frac{1}{2}\alpha^{T}W\;\alpha}} - {e^{T}\alpha}} & (11) \\{{{s.t.\mspace{14mu} 0} \leq \alpha_{i} \leq C},{i = 1},\ldots\mspace{14mu},\ell} & (12) \\{{y^{T}\alpha} = 0} & (13) \\{W_{ij} = {y_{i}y_{j}{K\left( {x_{i},x_{j}} \right)}}} & (14) \\{{K\left( {x_{i},x_{j}} \right)} = {\exp\left\{ {{- \gamma}{{x_{i} - x_{j}}}^{2}} \right\}}} & (15)\end{matrix}$This is a common formulation of the dual-problem for SVM optimization,which could be part of a standard library (LIBSVM) utilized to performthe computation. Here, α denotes an l-dimensional vector of decisionvariables that are being minimized, and W denotes an l×l positivesemidefinite matrix where each element is described by Equation (14).Also, e is a vector of ones having a dimension of l, and y is anindicator function that returns a vector of dimension l with valuesbeing either +1 or −1. Further, K(•) is the radial basis kernel functionused in this particular setting, and is the number of observations forthe two classes (true positives and false positives). Note, however,that other classifiers could also be used to classify stage one alertsinto true positive and false positive categories.

The model 434 is used during a stage two evaluation operation 442 toclassify new stage one alerts 428 and possibly generate stage two alerts444. For example, when a new stage one alert 428 is identified because acurrent statistical value 440 exceeds the threshold value 426, the SVMdetermines whether the new alert falls into the false positive class orthe true positive class. A stage two alert 444 could be generated onlywhen a stage one alert 428 that is a true positive is active. Once thestage two alert 444 has been activated, it could remain active for theentire duration of the stage one alert.

The stage one alerts 428 and/or the stage two alerts 444 could be usedin any suitable manner. For example, the alerts 428, 444 could be usedto generate output data 446 that is presented for graphical display 448(such as for presentation on the display device 224). The informationthat is displayed or otherwise sent to users could include any suitabledata, such as the existence of a stage one alert 428 or a stage twoalert 444. Additional information could also be provided, such as apossible root cause of the alert or a potential solution to the alert.The alerts could be used in any other suitable manner.

Note that the stage one training process 404 could be repeated at aregular specified interval. For example, the stage one training process404 could be repeated every day to retrain the model(s) 424 in order toaccount for changing conditions and operational modes in the furnace orother equipment being monitored. The evaluation process 408 could occurcontinuously or at a shorter regular specified interval, such as whenrepeated every minute, to identify problems that develop duringoperation of the process equipment. The stage two training process 406could be event-driven, such as when retraining or recalibration of themodel 434 is performed after each actual flooding event in order toupdate the model 434.

In some embodiments, the re-training of the model(s) 424 in the stageone training process 404 follows a prescribed and parametric schema. Forexample, a model 424 may be generated using the seven most recent daysof historical data, but the two most recent days could be ignoredentirely. Of the remaining five days, the previously-mentionedoperational rules 412 can be used to remove inadequate data segmentsfrom use during training. Inadequate data segments could include anytime instant that violates the operational rules 412, along with datafrom 30 minutes leading up to such time instants and 180 minutesfollowing such time instants. Inadequate data segments could alsoinclude any segment between two inadequate segments that is shorter than300 minutes. Of course, other criteria for selecting data segments usedto generate a model and/or for identifying inadequate data segmentscould be used.

Although FIG. 4 illustrates one example of a method 400 for detection offurnace flooding or other conditions, various changes may be made toFIG. 4. For example, while shown as a series of steps, various steps inFIG. 4 could overlap, occur in parallel, occur in a different order, oroccur any number of times. As a particular example, the evaluationprocess 408 could continue executing while the stage one trainingprocess 404 is occurring.

FIG. 5 illustrates an example technique 500 for retraining a model usedto detect furnace flooding or other conditions according to thisdisclosure. This technique 500 could, for example, be used during thestage one training process 404 to generate a model 424 in FIG. 4.

As shown in FIG. 5, the technique 500 uses data from a defined timeperiod 502, such as data collected during the preceding week. A blackoutperiod 504 excludes the most recent data, such as data collected duringthe preceding two days. The remaining period 506 includes data thatcould be used as training data during the model training process.

A line 508 here denotes a specific process variable signal, and limits510 define the operational range for the process variable signal. Thelimits 510 could, for example, be defined using one or more of the rules412. As specific examples, operational ranges could be placed on processvariable signals identifying combustible levels, draft levels, excessoxygen (O₂) levels, burner pressure levels, and outlet temperaturelevels of a furnace.

Data segments 512 within the period 506 are identified as acceptabletraining data since the line 508 remains within the operational rangedefined by the limits 510. However, a data segment 514 is excluded fromuse as training data since the line 508 falls outside the operationalrange defined by the limits 510. Moreover, small data segments 516 thatimmediately lead and trail the excluded data segment 514 (such as 30minutes leading up to the start of the excluded data segment 514 and 180minutes following the end of the excluded data segment 514) can also beexcluded from use as training data. The data within the non-excludeddata segments 512 can then be processed as described above to generateone or more models 424.

Although FIG. 5 illustrates one example of a technique 500 forretraining a model used to detect furnace flooding or other conditions,various changes may be made to FIG. 5. For example, the specific timevalues provided above are examples only. Also, other criteria could beused to include or exclude data. In addition, FIG. 5 illustrates aspecific non-limiting example of how data could be selected for use inmodel retraining, other data could be used in a similar manner for modelretraining.

FIG. 6 illustrates an example of second stage processing used to detectfurnace flooding or other conditions according to this disclosure. Asnoted above, the stage two evaluation operation 442 generally operatesto classify stage one alerts 428 into true positives (indicative offurnace flooding or other problem) and false positives (not indicativeof furnace flooding or other problem).

As shown in FIG. 6, time is initially divided into periods 602 with nostage one alerts and periods 604 with stage one alerts that areclassified as false positives. For example, a support vector machineusing the model(s) 434 could determine that the stage one alerts duringthe periods 604 are contained within the class of non-flood-leadingconditions. However, during a period 606, one or more stage one alertsare now classified as true positives, which are leading indicators of anactual flooding event 608. As can be seen here, the second stageprocessing can eliminate a number of false positive alerts while stillproviding sufficient lead time for operators to respond to and possiblyavoid the flooding event.

Although FIG. 6 illustrates one example of second stage processing usedto detect furnace flooding or other conditions, various changes may bemade to FIG. 6. For example, FIG. 6 illustrates a specific non-limitingexample of how specific alerts can be handled by the second stageprocessing, and any other alerts could be handled by the second stageprocessing.

In some embodiments, various functions described in this patent documentare implemented or supported by a computer program that is formed fromcomputer readable program code and that is embodied in a computerreadable medium. The phrase “computer readable program code” includesany type of computer code, including source code, object code, andexecutable code. The phrase “computer readable medium” includes any typeof medium capable of being accessed by a computer, such as read onlymemory (ROM), random access memory (RAM), a hard disk drive, a compactdisc (CD), a digital video disc (DVD), or any other type of memory. A“non-transitory” computer readable medium excludes wired, wireless,optical, or other communication links that transport transitoryelectrical or other signals. A non-transitory computer readable mediumincludes media where data can be permanently stored and media where datacan be stored and later overwritten, such as a rewritable optical discor an erasable storage device.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “application”and “program” refer to one or more computer programs, softwarecomponents, sets of instructions, procedures, functions, objects,classes, instances, related data, or a portion thereof adapted forimplementation in a suitable computer code (including source code,object code, or executable code). The term “communicate,” as well asderivatives thereof, encompasses both direct and indirect communication.The terms “include” and “comprise,” as well as derivatives thereof, meaninclusion without limitation. The term “or” is inclusive, meaningand/or. The phrase “associated with,” as well as derivatives thereof,may mean to include, be included within, interconnect with, contain, becontained within, connect to or with, couple to or with, be communicablewith, cooperate with, interleave, juxtapose, be proximate to, be boundto or with, have, have a property of, have a relationship to or with, orthe like. The phrase “at least one of,” when used with a list of items,means that different combinations of one or more of the listed items maybe used, and only one item in the list may be needed. For example, “atleast one of: A, B, and C” includes any of the following combinations:A, B, C, A and B, A and C, B and C, and A and B and C.

The description in the present application should not be read asimplying that any particular element, step, or function is an essentialor critical element that must be included in the claim scope. The scopeof patented subject matter is defined only by the allowed claims.Moreover, none of the claims invokes 35 U.S.C. § 112(f) with respect toany of the appended claims or claim elements unless the exact words“means for” or “step for” are explicitly used in the particular claim,followed by a participle phrase identifying a function. Use of termssuch as (but not limited to) “mechanism,” “module,” “device,” “unit,”“component,” “element,” “member,” “apparatus,” “machine,” “system,”“processor,” or “controller” within a claim is understood and intendedto refer to structures known to those skilled in the relevant art, asfurther modified or enhanced by the features of the claims themselves,and is not intended to invoke 35 U.S.C. § 112(f).

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Accordingly,the above description of example embodiments does not define orconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure, as defined by the following claims.

What is claimed is:
 1. A method for generating timely alerts to moveequipment in an industrial process to a safe operating conditioncomprising: obtaining data associated with an operation of equipment inan industrial process, wherein the data is obtained utilizing at leastone sensor comprising a structure for measuring at least onecharacteristic in or associated with the equipment in the industrialprocess, the data including an operating condition of the equipment inthe industrial process; identifying training data and evaluation data inthe obtained data; during each of multiple training periods, identifyingone or more models and one or more first statistical values using atleast some of the training data and determining a threshold value usingthe one or more first statistical values, the one or more modelsrepresenting the operation of the equipment; during each of multipleevaluation periods, determining one or more second statistical valuesusing at least some of the evaluation data and the one or more models,wherein determining the one or more second statistical values for one ofthe evaluation periods comprises, further comprises: normalizing each ofmultiple process variable signals using signal parameters estimatedduring a preceding training period, the process variable signalsidentifying values of process variables associated with the equipment;generating predictions for the process variable signals based onregression coefficients estimated during the preceding training period;identifying errors between the predictions and the normalized processvariable signals; scaling the errors based on standard deviations ofresiduals estimated during the preceding training period; for each ofmultiple time instants during the evaluation period, identifying a sumof squared scaled residuals; and identifying multiple second statisticalvalues for the multiple time instants using the sum of the squaredscaled residuals, comparing the one or more second statistical values tothe threshold value determined in a preceding one of the trainingperiods, and determining whether the equipment is suffering from atleast one specified condition based on the comparison; and in responseto determining that the equipment is suffering from the at least onespecified condition, generating a timely alert identifying the at leastone specified condition to move the equipment in an industrial processto a safe operating condition.
 2. The method of claim 1, whereindetermining the one or more first statistical values for one of thetraining periods comprises: estimating distribution parameters formultiple process variable signals, the process variable signalsidentifying values of process variables associated with the equipment;normalizing the process variable signals using the distributionparameters; performing multivariate regression for the normalizedprocess variable signals to generate regression equations; usingregression coefficients of the regression equations, identifyingstandard deviations of resulting residuals for the regression equations;scaling the residuals using the standard deviations; for each ofmultiple time instants during the training period, identifying the sumof squared scaled residuals; and identifying multiple first statisticalvalues for the multiple time instants using the sum of squared scaledresiduals.
 3. The method of claim 2, wherein determining the thresholdvalue for one of the training periods comprises: identifyingdistribution parameters, a mean, and a standard deviation of themultiple first statistical values generated for the training period; andidentifying the threshold value based on the distribution parameters,the mean, and the standard deviation of the multiple first statisticalvalues.
 4. The method of claim 1, wherein the at least one sensorcomprises at least one of: an oxygen sensor that measures an oxygenlevel located at at least one location with the equipment; a pressuresensor that measures a pressure level at the at least one locationwithin the equipment; a temperature sensor that measures a temperatureat the at least one location within the equipment or which measures atemperature of a process fluid being heated by the equipment; a sensorthat measures an amount of combustible material at the at least onelocation within the sensors; and a sensor that measures a flow ofmaterial into the equipment.
 5. The method of claim 1, wherein: theevaluation periods occur at a first interval; and the training periodsoccur at a second interval longer than the first interval and areperformed to retrain the one or more models to changing conditions oroperational modes of the equipment.
 6. The method of claim 1, whereinidentifying the training data and the evaluation data comprises:identifying data associated with a specified length of time preceding acurrent time; discarding data in a most-recent segment of the specifiedlength of time; in a remaining portion of the specified length of time,discarding data associated with a segment of time when a processvariable signal is outside of a specified range; and using non-discardeddata in the remaining portion of the specified length of time as thetraining data for a current training period.
 7. The method of claim 6,wherein identifying the training data and the evaluation data furthercomprises at least one of: discarding a segment of the data immediatelybefore and a segment of the data immediately after the segment of timewhen the process variable signal is outside of the specified range; anddiscarding a segment of the data spanning less than a threshold amountof time located between two discarded segments of data.
 8. The method ofclaim 1, wherein: obtaining the data comprises obtaining measurementsfrom multiple sensors including the at least one sensor, wherein atleast two of the multiple sensors capture measurements at differentfrequencies; and the method further comprises synchronizing themeasurements from the at least two of the multiple sensors to a commontime scale.
 9. The method of claim 1, wherein: the equipment in theindustrial process comprises a furnace; the obtained data comprisesmeasurements from multiple sensors associated with the furnace; and theat least one specified condition comprises flooding of the furnace. 10.The method of claim 9, wherein obtaining the data, identifying thetraining data and the evaluation data, identifying the one or moremodels, determining whether the equipment is suffering from the at leastone specified condition, and generating the alert are repeated for eachof multiple furnaces.
 11. An apparatus for generating timely alerts tomove equipment in an industrial process to a safe operating conditioncomprising: at least one processing device configured to: obtain dataassociated with operation of equipment in an industrial process, whereinthe data is obtained utilizing at least one sensor comprising astructure for measuring at least one characteristic in or associatedwith the equipment in the industrial process, the data including anoperating condition of the equipment in the industrial process; identifytraining data and evaluation data in the obtained data; during each ofmultiple training periods, identify one or more models and one or morefirst statistical values using at least some of the training data anddetermine a threshold value using the one or more first statisticalvalues, the one or more models representing the operation of theequipment; during each of multiple evaluation periods, determine one ormore second statistical values using at least some of the evaluationdata and the one or more models wherein determining the one or moresecond statistical values for one of the evaluation periods, furthercomprises: normalizing each of multiple process variable signals usingsignal parameters estimated during a preceding training period, theprocess variable signals identifying values of process variablesassociated with the equipment; generating predictions for the processvariable signals based on regression coefficients estimated during thepreceding training period; identifying errors between the predictionsand the normalized process variable signals; scaling the errors based onstandard deviations of residuals estimated during the preceding trainingperiod; for each of multiple time instants during the evaluation period,identifying a sum of squared scaled residuals; and identifying multiplesecond statistical values for the multiple time instants using the sumof the squared scaled residuals, comparing the one or more secondstatistical values to the threshold value determined in a preceding oneof the training periods, and determining whether the equipment issuffering from at least one specified condition based on the comparison;and in response to determining that the equipment is suffering from theat least one specified condition, generate a timely alert identifyingthe at least one specified condition to move the equipment in anindustrial process to a safe operating condition.
 12. The apparatus ofclaim 11, wherein, to determine the one or more first statistical valuesfor one of the training periods, the at least one processing device isconfigured to: estimate distribution parameters for multiple processvariable signals, the process variable signals identifying values ofprocess variables associated with the equipment; normalize the processvariable signals using the distribution parameters; perform multivariateregression for the normalized process variable signals to generateregression equations; use regression coefficients of the regressionequations, identifying standard deviations of resulting residuals forthe regression equations; scale the residuals using the standarddeviations; for each of multiple time instants during the trainingperiod, identify a sum of squared scaled residuals; and identifymultiple first statistical values for the multiple time instants usingthe sums.
 13. The apparatus of claim 12, wherein, to determine thethreshold value for one of the training periods, the at least oneprocessing device is configured to: identify distribution parameters, amean, and a standard deviation of the multiple first statistical valuesgenerated for the training period; and identify the threshold valuebased on the distribution parameters, the mean, and the standarddeviation of the multiple first statistical values.
 14. The apparatus ofclaim 11, wherein the at least one sensor comprises at least one of: anoxygen sensor that measures an oxygen level located at at least onelocation with the equipment; a pressure sensor that measures a pressurelevel at the at least one location within the equipment; a temperaturesensor that measures a temperature at the at least one location withinthe equipment or which measures a temperature of a process fluid beingheated by the equipment; a sensor that measures an amount of combustiblematerial at the at least one location within the sensors; and a sensorthat measures a flow of material into the equipment.
 15. The apparatusof claim 11, wherein: the equipment in the industrial process comprisesa furnace; the obtained data comprises measurements from multiplesensors associated with the furnace including the at least one sensoramong the multiple sensors; and the at least one specified conditioncomprises flooding of the furnace.
 16. The apparatus of claim 15,wherein the measurements comprise measurements of combustible levels,draft levels, oxygen (O₂) levels, pressure levels, and temperaturelevels associated with the furnace.
 17. A non-transitory computerreadable medium for generating timely alerts to move equipment in anindustrial process to a safe operating condition containing instructionsthat when executed cause at least one processing device to: obtain dataassociated with operation of equipment in an industrial process, whereinthe data is obtained utilizing at least one sensor comprising astructure for measuring at least one characteristic in or associatedwith the equipment in the industrial process, the data including anoperating condition of the equipment in the industrial process; identifytraining data and evaluation data in the obtained data; during each ofmultiple training periods, identify one or more models and one or morefirst statistical values using at least some of the training data anddetermine a threshold value using the one or more first statisticalvalues, the one or more models representing the operation of theequipment; during each of multiple evaluation periods, determine one ormore second statistical values using at least some of the evaluationdata and the one or more models, wherein determining of the one or moresecond statistical values for one of the evaluation periods, furthercomprises: normalizing each of multiple process variable signals usingsignal parameters estimated during a preceding training period, theprocess variable signals identifying values of process variablesassociated with the equipment; generating predictions for the processvariable signals based on regression coefficients estimated during thepreceding training period; identifying errors between the predictionsand the normalized process variable signals; scaling the errors based onstandard deviations of residuals estimated during the preceding trainingperiod; for each of multiple time instants during the evaluation period,identifying a sum of squared scaled residuals; and identifying multiplesecond statistical values for the multiple time instants using the sumof the squared scaled residuals, comparing the one or more secondstatistical values to the threshold value determined in a preceding oneof the training periods, and determining whether the equipment issuffering from at least one specified condition based on the comparison;and in response to determining that the equipment is suffering from theat least one specified condition, generate an alert identifying the atleast one specified condition.
 18. The non-transitory computer readablemedium of claim 17, wherein the instructions that when executed causethe at least one processing device to determine the one or more firststatistical values for one of the training periods comprise instructionsthat when executed cause the at least one processing device to: estimatedistribution parameters for multiple process variable signals, theprocess variable signals identifying values of process variablesassociated with the equipment; normalize the process variable signalsusing the distribution parameters; perform multivariate regression forthe normalized process variable signals to generate regressionequations; use regression coefficients of the regression equations,identifying standard deviations of resulting residuals for theregression equations; scale the residuals using the standard deviations;for each of multiple time instants during the training period, identifya sum of squared scaled residuals; and identify multiple firststatistical values for the multiple time instants using the sums. 19.The non-transitory computer readable medium of claim 18, wherein theinstructions that when executed cause the at least one processing deviceto determine the threshold value for one of the training periodscomprise instructions that when executed cause the at least oneprocessing device to: identify distribution parameters, a mean, and astandard deviation of the multiple first statistical values generatedfor the training period; and identify the threshold value based on thedistribution parameters, the mean, and the standard deviation of themultiple first statistical values.
 20. The non-transitory computerreadable medium of claim 17, wherein the at least one sensor comprisesat least one of: an oxygen sensor that measures an oxygen level locatedat at least one location with the equipment; a pressure sensor thatmeasures a pressure level at the at least one location within theequipment; a temperature sensor that measures a temperature at the atleast one location within the equipment or which measures a temperatureof a process fluid being heated by the equipment; a sensor that measuresan amount of combustible material at the at least one location withinthe sensors; and a sensor that measures a flow of material into theequipment.